library(here); library(sf); library(ggplot2); library(ggmap); library(leaflet);
library(tmap) ## NOTE CED: Ich habe jetzt die Lribaries immer dann geladen, wenn sie notwendig sind; generell ist abtrennen mit ; übrigens als bad practice gelabeled, evtl nicht so zeigen

animal <- readRDS(file = here('output/data-proc/tag5334_gps_proc.Rds')) 

## transform into spatial simple feature sf object
mydf_sf <- st_as_sf(x = data.frame(animal),
                    coords = c("longitude", "latitude"),
                    crs = 4326,
                    sf_column_name = "geometry" )
## and project
mydf_sf_trans <- st_transform(mydf_sf, 5631)  ## EPSG-code Pulkovo

## latitude is y-Axis, longitude = x-axis in cartesian coordinate system
plot(animal$latitude ~ animal$longitude,
     pch = ".",
     col = as.numeric(animal$daytime) + 1) ## + 1 because color 0 is transparent

plot(animal$latitude[1:50] ~ animal$longitude[1:50], type= 'l') 

baseplot_df <- 
  ggplot(data = animal, 
         aes(x = longitude,
             y = latitude,
             color = daytime)) +
    geom_point(size = 0.01, alpha = 0.5) +  
    labs(x = "Longitude", y = "Latitude",
         title = "Telemetry data") +
    theme_bw(base_size = 16)

baseplot_df

baseplot_sf <- 
  ggplot(data = mydf_sf_trans, 
         aes(color = daytime)) +
    geom_sf(size = 0.01, alpha = 0.5) +  
    labs(x = "Longitude", y = "Latitude",
         title = "Telemetry data") +
    theme_bw(base_size = 16)

baseplot_sf

densplot <- 
  baseplot_sf + 
  stat_density_2d(
    aes(x = st_coordinates(mydf_sf_trans)[,1], 
        y = st_coordinates(mydf_sf_trans)[,2], 
        alpha = ..level..,
        color = daytime,
        color = after_scale(colorspace::darken(color, .5, space = "HLS")),   ## was soll das machen? wird nicht angewandt , da selbst-referenziell (color = dunkle version von color = dunkle version von dunkler version von color etc); wenn es daytime sein soll, muss es nochmal explizit angesprochen werden
        fill  = after_scale(colorspace::lighten(color, .4, space = "HLS"))), ## was soll das machen? 
     geom  = "polygon",
     #color = "white" , ## color wird überschrieben -> einmal in aes() definiert, einmal außerhalb -> daher weiß
     size  = .02,
     n     = 500,
     bins  = 5,
     adjust = 3)  

densplot

# example of how to save the plot to your file
# ggsave(here("plots", "plot_pointdensity.png"), dpi = 800, height = 6, width = 6)

# define a bounding box for the plot
b <- c(13.460,52.485,13.490,52.500) # make large enough to plot all data!

my_tiles <- get_map(location = b, maptype = "terrain", source = "osm", zoom = 15)

ggmap(my_tiles) + 
  geom_point(aes(x = longitude, y = latitude), 
             data = data.frame(animal), 
             colour = as.numeric(animal$daytime)+6, 
             size = 0.05) + 
  labs(x = "Latitude", y = "Longitude", title = "Telemetry locations")

# get first location of track
base_wgs84_x <- animal$longitude[1]
base_wgs84_y <- animal$latitude[1]

m_leaf <- leaflet(animal) %>% 
  addTiles() %>%
  addMarkers(lng=base_wgs84_x, lat=base_wgs84_y, 
             popup="Start") %>%
  addPopups(base_wgs84_x, base_wgs84_y, 'Starting point',
            options = popupOptions(closeButton = TRUE))%>%
  addCircles(lng = ~longitude, lat = ~latitude,~5,
             stroke = T,
             color= 'white',
             weight=1,
             fill=T, 
             fillColor= 'blue',
             fillOpacity = 0.3) 

m_leaf # leaflet is bad for export -> create maps in ggmap instead!

tmap_mode(mode = "plot")
  tm_shape(shp = mydf_sf_trans) + 
    tm_dots(size = 0.01, 
            col = "daytime",  
            alpha = 0.5)